Henk van Waarde
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Affiliation |
I am an assistant professor at the University of Groningen, where I work in the Systems, Control and Optimization group within the Bernoulli Institute.
I am also a member of the Centre for Data Science and Systems Complexity and the
Jan C. Willems Center for Systems and Control.
For my bio and a summary of my CV, check out this page.
Also feel free to browse through my publications, presentations and updates linked below.
News
August 2023: I am grateful to the Dutch Research Council for funding my VENI project!
April 2023: Our overview paper on the informativity framework was conditionally accepted for publication in the IEEE Control Systems!
December 2022: I gave three presentations at the IEEE Conference on Decision and Control, slides are available here.
July 2022: I am honored to receive the EECI PhD Award! The prize is awarded anually by the European Embedded Control Institute.
June 2022: Very happy to share our new results on a behavioral approach to data-driven control, using the concept of quadratic difference forms.
January 2022: I am honored to receive the 2021 Bernoulli Institute's Best Ph.D. Thesis Award in Mathematics!
December 2021: I am honored to receive the 2021 IEEE Control Systems Letters Outstanding Paper Award for this paper, coauthored with C. De Persis, K. Camlibel and P. Tesi!
Research
My research interests lie in the field of systems and control. I aim to develop a systems and control theory that is grounded on measured data. I work towards mapping raw data into models and control policies with rigorous guarantees on accuracy, stability and performance, while answering key questions such as: how much data is needed? how to handle noise? and how to perform control-relevant experiments?
Some of the specific topics of interest are:
Direct data-driven control
System identifiability
Kernel-based identification of (physical) dynamical systems
Experiment design
Applications to networked systems and neuroscience
We have recently approached the question of how much data is required for control via the concept of informativity, which is explored for several control problems in:
To address robustness of controllers with respect to noise and bounded nonlinearities, I have worked on generalizations of the S-lemma and Finsler's lemma:
Quadratic matrix inequalities with applications to data-based control
From noisy data to feedback controllers: non-conservative design via a matrix S-lemma
A matrix Finsler's lemma with applications to data-driven control
Kernel-based modeling for systems with (incremental) dissipativity properties is treated in the papers:
Some other recent work involves the problem of experiment design:
A persistency of excitation condition for continuous-time systems
Beyond persistent excitation: online experiment design for data-driven modeling and control
Willems’ fundamental lemma for state-space systems and its extension to multiple datasets
A complete list of my publications and preprints can be found here or on my Google Scholar page